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Background: Head and neck squamous cell carcinomas (HNSCCs) are complex and highly aggressive tumours that develop in the mouth, throat, salivary glands and nose. HNSCCs account for more than half a million cases annually and are the sixth most common cancer worldwide. Alcohol, tobacco and human papilloma virus (HPV) infection are the well-known causes for HNSCC. The current options for treatment are surgery, radiotherapy, chemotherapy or a combination of therapies. Locally advanced HNSCC patients show heterogenous response to standard treatments and the survival after 5 years is about 50%. Therefore, there is a need to identify biomarkers to predict outcome and improve personalised therapies. The recent advancement in next generation sequencing technologies has allowed for understanding the molecular characteristics of the tumour and identify patients at high risk that are unresponsive to the standard treatment. HPV-associated oropharyngeal carcinoma have shown a very high rate of loco-regional control (LRC) and overall survival (OS) after postoperative radio- chemotherapy (PORT-C) and are being assessed for treatment de-escalation strategies to reduce toxicity in clinical trials. The treatment response of HPV-negative HNSCC, however, is still heterogeneous and novel biomarkers are required to identify subgroups of patients for treatment adaptation. Objectives: The overall aim of the thesis is to develop biomarkers to identify patients at high risk for future treatment adaptations and improve personalised radiotherapy based on the biological differences in HNSCC patients. For this purpose, novel gene signatures were developed and validated using machine learning approaches and biological information in order to predict LRC in patients with locally advanced HNSCC. The novel gene signatures will help to identify patients at high risk that do not respond to standard treatments and to further understand the molecular mechanisms involved in heterogenous treatment response. Materials and methods: The data from a total of 504 locally advanced HNSCC patients of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG) treated with postoperative radiotherapy (PORT) or postoperative radiochemotherapy (PORT-C) were evaluated. Data from 60 mice bearing xenografts of ten established human HNSCC cell lines were also evaluated. Gene expression analyses was performed using the GeneChip Human Transcriptome Array 2.0 and nanoString analyses. Differential gene expression analysis, Cox regression analysis and machine learning algorithms were used to develop gene signatures. Models were built on the training cohort and then applied on an independent validation cohort. Results: The patients with HPV-negative HNSCC that were treated with PORT-C were classified into the four molecular subtypes basal, mesenchymal, atypical and classical that were previously reported for HNSCC patients treated with primary radio(chemo)therapy or surgery and were related to LRC. The mesenchymal subtype had the worst prognosis as compared to the other subtypes. These tumours were associated with overexpression of epithelial-mesenchymal transition genes and DNA repair genes. A novel 6-gene signature was developed and validated based on full-transcriptome data using machine-learning approaches that was prognostic for LRC in patients with HPV-negative HNSCC treated with PORT-C. The 6-gene signature consisted of four individual genes CAV1, GPX8, IGLV3-25, TGFBI and one metagene combining the highly correlated genes, INHBA and SERPINE1. The identified gene signature was combined with the clinical parameters, T stage and tumour localisation as well as the stem-cell marker CD44 and the 15-gene hypoxia- associated classifier and this improved the performance of the model. Previously identified prognostic gene signatures and molecular-subtype classification were back-translated from HNSCC patients to pre-clinical tumour models. The tumour models were classified into the four subtypes basal, mesenchymal, atypical and classical, similar to the patients. The mesenchymal tumours were significantly associated with a higher TCD50 as compared to other subtypes. A novel 2-gene signature consisting of FN1 and SERPINE1 was developed based on tumour models and patient data using differential gene expression analysis. The 2-gene signature was prognostic for the TCD50 in tumour models and was successfully validated on an independent PORT-C patient cohort for LRC. A matched-pair analysis was performed between patients that were treated with postoperative radiochemotherapy and patients that were treated with postoperative radiotherapy. A 2- metagene signature, consisting of KRT6A, KRT6B, KRT6C forming one metagene and SPRR1A, SPRR1B, SPRR2A, SPRR2C forming the second metagene, was identified. The novel predictive signature stratified patients into high and low risk groups. The high-risk group patients that received PORT-C showed higher LRC as compared to the high-risk patients that received PORT. Thus, the predictive gene signature identified patients that were considered to be at intermediate risk according to clinical factors but were at biologically high risk for the development of loco-regional recurrences after PORT. These patients might benefit from PORT-C treatment. Conclusions: In this thesis, novel gene signatures were identified by combining machine learning and biological information to stratify locally advanced HNSCC patients into high and low risk groups for loco-regional control. This information could be used in the future, e.g. to adjust radiotherapy doses based on the risk group. The developed gene signatures could be combined with other gene signatures or the molecular subtype stratification to develop potential combined treatment approaches. Within the DKTK-ROG framework, the gene signatures will be incorporated with biomarkers developed on the same cohort at the other DKTK-ROG partner sites using the data from different omics platforms in the future. This would help to better understand the molecular basis of heterogenous treatment response in HNSCC patients and uncover novel targets for therapies. The thesis also provides a valuable insight into the applicability of preclinical tumour models to study the efficacy of personalised radiotherapy treatments. Overall, the gene signatures identified in this thesis were from retrospective studies and have to be validated in prospective studies before their application in interventional clinical trials to improve personalised radiotherapy treatments. Additionally, the methods used in the thesis to identify the gene signatures could be used and applied across different cancer datasets for identification of biomarkers. Therefore, this thesis has provided a basis for future studies on personalized treatment of HNSCC based on their genetic profile.:Content Abbreviations VII Tables XII 1 Introduction 1 2 Biological and statistical background 6 2.1 Head and neck squamous cell carcinoma 6 2.1.1 Tumourigenesis 6 2.1.2 Biomarkers: clinical and genomics 9 2.2 Statistics 12 General statistical analyses 17 2.3 Gene expression analyses 18 3 Molecular subtypes and mechanisms of radioresistance 20 3.1 Introduction and motivation 20 3.2 Patient cohort and experimental design 21 3.2.1 Patient cohort 21 3.2.2 Clinical endpoints and statistical analysis 23 3.2.3 Experimental design 23 3.3 Results 26 3.3.1 Prognostic factors for LRC and OS 26 3.3.2 Death as competing risk 26 3.3.3 Multivariable Cox regression for improved prognosis 29 3.3.4 Molecular subtypes in HPV-negative HNSCC patients 31 3.3.5 Molecular subtypes are prognostic for LRC after PORT-C 33 3.4 Discussion 36 4 A novel 6-gene signature for LRC prognosis 39 4.1 Introduction and motivation 39 4.2 Patient cohort and experimental design 40 4.2.1 Patient cohorts 40 4.2.2 Clinical endpoints and statistical analysis 41 4.2.3 Experimental design 41 4.3 Results 44 4.3.1 Characteristics of the patient cohorts 44 4.3.2 Development of the 6-gene signature prognostic for LRC 45 4.3.3 Combination of the 6-gene signature and clinical parameters 47 4.3.4 Extension with CD44 and the 15-gene hypoxia signature 48 4.3.5 Prognostic for secondary endpoints 49 4.3.6 Technical validation using nanoString technology 52 4.3.7 Death as competing risk 56 4.4 Discussion 58 5 Biomarker development in preclinical tumour models and HNSCC patients 62 5.1 Introduction and motivation 62 5.2 Patient cohort and experimental design 64 5.2.1 Patient derived xenograft tumour models 64 5.2.2 Patient cohorts 64 5.2.3 Clinical endpoints and statistical analysis 65 5.2.4 Experimental design 65 5.3 Results 68 5.3.1 Molecular subtypes 68 5.3.2 Development of the 2-gene signature 70 5.3.3 Technical validation using the nanoString technology 71 5.3.4 Back-translation of gene signatures in xenograft models 75 5.4 Discussion 79 6 PORT-C improves LRC in intermediate-risk patients 82 6.1 Introduction and motivation 82 6.2 Patient cohort and experimental design 83 6.2.1 Patient cohorts 84 6.2.2 Clinical endpoints and statistical analysis 84 6.2.3 Experimental design 84 6.3 Results 87 6.3.1 Characteristics of the patient cohorts 87 6.3.2 Propensity score matching analysis 88 6.3.3 Development of the predictive 2-metagene signature 90 6.4 Discussion 93 7 Conclusion and future perspectives 96 8 Summary 99 9 Zusammenfassung 102 Appendix 105 A. Supplementary Figures 105 B. Supplementary Tables 110 Bibliography 116 Erklärungen 149

Gastric and esophagogastric junction cancers are heterogeneous and aggressive tumors with an unpredictable response to cytotoxic treatment. New methods allowing for the analysis of drug resistance are needed. Here, we describe a novel technique by which human tumor specimens can be cultured ex vivo, preserving parts of the natural cancer microenvironment. Using a tissue chop- per, fresh surgical tissue samples were cut in 400 μm slices and cultivated in 6-well plates for up to 6 days. The slices were processed for routine histopa- thology and immunohistochemistry. Cytokeratin stains (CK8, AE1/3) were ap- plied for determining tumor cellularity, Ki-67 for proliferation, and cleaved caspase-3 staining for apoptosis. The slices were analyzed under naive conditions and following 2–4 days in vitro exposure to 5-FU and cisplatin. The slice culture technology allowed for a good preservation of tissue morphology and tumor cell integrity during the culture period. After chemotherapy exposure, a loss of tumor cellularity and an increase in apoptosis were observed. Drug sensitivity of the tumors could be assessed. Organotypic slice cultures of gastric and es- ophagogastric junction cancers were successfully established. Cytotoxic drug effects could be monitored. They may be used to examine mechanisms of drug resistance in human tissue and may provide a unique and powerful ex vivo platform for the prediction of treatment response.

Durch die Vielzahl von heterogenen Informationsquellen sehen sich viele Nutzer einer kaum überschaubaren Informationsflut gegenüber. Aus diesem Grund werden durch diese Arbeit die gängigen Nachrichtenformate analysiert und der aktuelle Stand der Technik im Bereich der Nachrichtenaggregatoren dargelegt. Dabei werden diese Analysen immer mit Blick auf die Möglichkeiten einer personalisierten Filterung der Inhalte durchgeführt. Anschließend wird eine im Rahmen dieser Arbeit entstandene Infrastruktur für die Aggregation, personalisierte Filterung und kollaborative Empfehlung von Inhalten aus heterogenen Nachrichtenquellen vorgestellt. Dabei wird detailiert auf die zu Grunde liegenden Konzepte eingegangen und deren praktische Umsetzung beschrieben.

Ziel: Entwicklung eines gesundheitsökonomischen Evaluationskonzepts zum Nachweis einer Kostenreduktion unter gleichzeitiger Optimierung des medizinischen Nutzens durch pharmakogenetisches Screening bei Erstdiagnose einer Schizophrenie. Finale Zielsetzung ist die Aufnahme der pharmakogenetischen a priori-Diagnostik für die Indikation Schizophrenie in die GKV-Regelversorgung. Methodik: Basierend auf dem aktuellen Stand gendiagnostischer Forschung sowie der evidenzbasierten Schizophrenietherapie wurde eine prospektive, randomisierte und kontrollierte, dreiarmige, offene, multizentrische Pilotstudie im Paralleldesign über 3 Jahre konzeptioniert. Studienpopulation: 300 Patienten (1:1:1) im Alter von 18 bis 65 Jahren mit erstmaliger F20-Diagnose (ICD-10). Interventionen: pharmakogenetisches Screening und integrierte Versorgung; integrierte Versorgung; Standardversorgung. Die Erhebung des medizinischen Nutzens erfolgt durch Messung des klinischen Outcome bzgl. der patientenrelevanten Endpunkte Mortalität, Morbidität, Lebensqualität und Nebenwirkungen zu definierten Zeitpunkten. Perspektivisch relevante Kosten werden im "piggy back"-Verfahren ermittelt. Ergebnisse: Angesichts zurzeit bestehender Limitationen im deutschen Gesundheitssystem (z.B. unzureichendes intersektorales Schnittstellenmanagement bei der Arzneimittelversorgung und Informationsweitergabe) wird die Integrierte Versorgung als geeignete Versorgungsform für den Nutzennachweis eingestuft. Die Integrierte Versorgung stellt jedoch momentan nicht den allgemeinen Standard der psychiatrischen Patientenversorgung dar. Aus GKV-Perspektive wesentliche Kostentreiber der Schizophrenietherapie sind Rückfälle, Krankenhausaufenthalte, Arbeitslosigkeit und vorzeitige Verrentung. Eine Verringerung der Häufigkeit dieser Parameter könnte z.B. zu einer Reduktion der Erstjahres-Behandlungskosten (zurzeit ca. 30% der Gesamtkosten) führen. Die Kosten-Effektivitäts-Analyse erweist sich als Studienform mit der geringsten Anfälligkeit für Bias und Confounder. Trotz einer vergleichsweise hohen externen Validität ist das Studiensetting nicht uneingeschränkt übertragbar auf die Versorgungsrealität des deutschen Gesundheitssystems. Es existiert aktuell keine generelle Empfehlung für den Einsatz der Gendiagnostik zur Steuerung der Arzneimitteltherapie in Psychiatrie. Ebenso hat die integrierte Versorgung bisher keinen umfassenden Einzug in den psychiatrischen Behandlungsalltag gefunden, so dass die beschriebenen Limitationen einen positiven Nutzennachweis erschweren. Dennoch ist das Konzept als praktisch umsetzbar zu bewerten. Schlussfolgerung: Bei dieser Faktenlage ist das Interesse der GKV an der Veranlassung einer gesundheitsökonomischen Evaluation mit dem Ziel einer Erstattungsfähigkeit des a priori durchgeführten pharmakogenetischen Screenings bei Schizophrenie als eher gering einzustufen. Jedoch lassen das Update der S3-Praxisleitlinie mit dem Einbezug der strukturierten u. integrierten Versorgung sowie der Aktionsplan „Individualisierte Medizin“ des Bundesforschungsministeriums auf eine Fokussierung auf diese Fragestellung und veränderte Interessenlage bzgl. der Initiierung der Pilotstudie hoffen. Weitere Forschungstätigkeit sowie die praktische Erprobung neuer gendiagnostischen Verfahren sind, basierend auf versorgungsbezogenen Pilotstudien wie der hier konzeptionierten, fachübergreifend erforderlich, um die Relevanz der Methodik für den psychiatrischen Versorgungsalltag zu belegen. / Purpose: Development of a health-economic investigation method to study whether a cost reduction under concurrent optimisation of the medical use exists by using pharmacogenetic a- priori- screening with first diagnosis of a schizophrenia. Final objective is the reimbursement of pharmacogenetic diagnostics for the indication schizophrenia in the German health statutory insurance (GKV). Methods: A prospective, randomised and controlled, 3-armed, parallel, open, multicentre pilot study with a duration of 3 years was designed based on the actual status of genetic-diagnostic research as well as the evidence-based therapy of schizophrenia. Study population: 300 patients (1:1:1) aged 18 to 65 years with initial F20 diagnosis (ICD-10). Interventions: pharmacogenetic screening and integrated care; integrated care; standard care. For evaluation of the medical benefit the clinical outcome is measured at defined times with regard to the patients' relevant endpoints mortality, morbidity, quality of life and side effects. In perspective relevant costs are determined by "piggy back" procedure. Results: In view of actually existing limitations within the German health system (e.g., insufficient intersectional medication and information management) the integrated care is considered being a suitable setting to demonstrate the advantage of using pharmacogenetic screening. Nevertheless, the integrated care does not show the general standard of the psychiatric patient's care at the moment. From GKV perspective essential cost drivers of schizophrenia therapy are relapses, hospital stays, unemployment and untimely superannuation. Diminishing the rate of these parametres could lead, e.g., to a reduction of the first year medical costs (at the moment approx. 30% of the total expenses). The cost-effectiveness analysis seems to be the study form with the slightest susceptibility to bias and confounding. In spite of a relatively high external validity the study setting is not unconditionally transferable to the German health system. Currently no general recommendation exists for the application of the genetic diagnostics to manage medication therapy in psychiatry. Up to now also the integrated care has not found a comprehensive entry in psychiatric practice, so that the described limitations are complicating a positive use proof. Nevertheless, the investigational concept can be regarded as feasible. Conclusion: Based on the existing situation the GKV's interest in performing a health-economic evaluation, which is focussed on the reimbursement of pharmacogenetic a priori-diagnostics in schizophrenia, is considered to be low. However, the situation may change in view of the expected update of the S3-practise guideline with the focus on structured and integrated care as well as the action plan „individualised medicine“ of the German federal research ministry. Thus, there is hope for changing interests in a pilot study. Based on care-related pilot studies as presented here, further research activities and practical testing of recent gene diagnostic procedures are necessary to demonstrate the relevance of the methodology for psychiatric practice.

Gastric and esophagogastric junction cancers are heterogeneous and aggressive tumors with an unpredictable response to cytotoxic treatment. New methods allowing for the analysis of drug resistance are needed. Here, we describe a novel technique by which human tumor specimens can be cultured ex vivo, preserving parts of the natural cancer microenvironment. Using a tissue chop- per, fresh surgical tissue samples were cut in 400 μm slices and cultivated in 6-well plates for up to 6 days. The slices were processed for routine histopa- thology and immunohistochemistry. Cytokeratin stains (CK8, AE1/3) were ap- plied for determining tumor cellularity, Ki-67 for proliferation, and cleaved caspase-3 staining for apoptosis. The slices were analyzed under naive conditions and following 2–4 days in vitro exposure to 5-FU and cisplatin. The slice culture technology allowed for a good preservation of tissue morphology and tumor cell integrity during the culture period. After chemotherapy exposure, a loss of tumor cellularity and an increase in apoptosis were observed. Drug sensitivity of the tumors could be assessed. Organotypic slice cultures of gastric and es- ophagogastric junction cancers were successfully established. Cytotoxic drug effects could be monitored. They may be used to examine mechanisms of drug resistance in human tissue and may provide a unique and powerful ex vivo platform for the prediction of treatment response.

One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approach for the correction of intensity non-uniformity in magnetic resonance imaging data was evolved to improve image quality prior to feature computation. Further, different feature selection methods and machine learning algorithms for time-to-event survival data were evaluated to identify suitable algorithms for radiomics risk modelling. An improved model performance could be demonstrated using computed tomography data, which were acquired during the course of treatment. Subsequently tumour sub-volumes were analysed and it was shown that the tumour rim contains the most relevant prognostic information compared to the corresponding core. The incorporation of such spatial diversity information is a promising way to improve the performance of risk models.:1. Introduction 2. Theoretical background 2.1. Basic physical principles of image modalities 2.1.1. Computed tomography 2.1.2. Magnetic resonance imaging 2.2. Basic principles of survival analyses 2.2.1. Semi-parametric survival models 2.2.2. Full-parametric survival models 2.3. Radiomics risk modelling 2.3.1. Feature computation framework 2.3.2. Risk modelling framework 2.4. Performance assessments 2.5. Feature selection methods and machine learning algorithms 2.5.1. Feature selection methods 2.5.2. Machine learning algorithms 3. A physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging 3.1. Intensity non-uniformity correction methods 3.2. Physical correction model 3.2.1. Correction strategy and model definition 3.2.2. Model parameter constraints 3.3. Experiments 3.3.1. Phantom and simulated brain data set 3.3.2. Clinical brain data set 3.3.3. Abdominal data set 3.4. Summary and discussion 4. Comparison of feature selection methods and machine learning algorithms for radiomics time-to-event survival models 4.1. Motivation 4.2. Patient cohort and experimental design 4.2.1. Characteristics of patient cohort 4.2.2. Experimental design 4.3. Results of feature selection methods and machine learning algorithms evaluation 4.4. Summary and discussion 5. Characterisation of tumour phenotype using computed tomography imaging during treatment 5.1. Motivation 5.2. Patient cohort and experimental design 5.2.1. Characteristics of patient cohort 5.2.2. Experimental design 5.3. Results of computed tomography imaging during treatment 5.4. Summary and discussion 6. Tumour phenotype characterisation using tumour sub-volumes 6.1. Motivation 6.2. Patient cohort and experimental design 6.2.1. Characteristics of patient cohorts 6.2.2. Experimental design 6.3. Results of tumour sub-volumes evaluation 6.4. Summary and discussion 7. Summary and further perspectives 8. Zusammenfassung

Einleitung: Die vorgelegte retrospektive Studie fokussiert sich auf Multiple Sklerose-Patient:- innen, die mit Glatiramerazetat behandelt wurden. Analysiert wurden klinische und immuno- logische Parameter. Es wurde geprüft, ob ein Zusammenhang zwischen den Glatiramerazetat spezifischen Antikörpern der Klassen IgM und IgG (Subklassen 1-4) sowie der Konzentration von Neurofilament (NfL) im Serum mit etablierten klinischen Markern besteht, mit dem Ziel, potenzielle Biomarker für die Krankheitsaktivität und somit für das Versagen der Glatiramer- azetattherapie zu evaluieren. Material und Methoden: Das Patientenkollektiv umfasste 56 Patient:innen mit RRMS. Von ihnen waren 72 % therapienaiv, 28 % waren mit Interferon-beta vorbehandelt. Als klinische Parameter wurden EDSS, MSFC und die Anwesenheit von Schüben erfasst. Schubaktivität und Bestimmung von Immunglobulinen und NfL wurden bei neun Visiten erhoben, der EDSS und der MFSC zum Studienbeginn und nach zwölf Monaten. Die Immunglobulinkonzentration wurde mittels ELISA, NfL mit der SiMoA-Technologie bestimmt. Ergebnisse: Glatiramerazetat spezifische Antikörper wurden in unterschiedlichen Ausprägung produziert. Die IgG-Produktion klinisch stabiler Patient:innen war sowohl im M12 als auch im M24 deutlich höher als zum Beginn der Therapie (p < .001). Bis auf die Subklasse IgG3 hatte die Vortherapie keinen Einfluss auf die Antikörper-Produktion. Hinsichtlich des klinischen Outcomes korreliert die absteigende Konzentration von IgG2 mit einer mindestens 20 %-igen Besserung im MSFC nach zwölf Monaten der Glatiramerazetatbehandlung (r = -.301, p = .013). In der Subklasse IgG4 weist die absteigende Konzentration auf stabile oder bessere EDSS-Werte nach zwölfmonatiger Behandlung hin. Unter Glatiramerazetattherapie zeigte sich eine Reduktion des NfL-Spiegels, allerdings nicht signifikant. Die vortherapierten Patient:innen wiesen signifikant höhere Konzentrationen von NfL als therapienaive Patient:innen auf (p < .001). Es ergaben sich keine Unterschiede der NfL-Konzentration zwischen Patient:innen mit und ohne Schubaktivität sowie keine Korrelationen mit anderen klinischen Outcomes. Diskussion: Die Ergebnisse deuten darauf hin, dass glatiramerazetatspezifische Antikörper und NfL-Spiegel eine Rolle als Biomarker für klinische Aktivität bei mit Glatiramerazetat behandelten Patient:innen spielen könnten und somit unterstützend im Monitoring der Wirksamkeit des Medikaments sein können.:Inhalt Abkürzungsverzeichnis 5 Abstrakte 8 Deutsche Version 8 English Version 9 1 Einleitung und theoretische Grundlagen 10 1.1 Bedeutung der Multiplen Sklerose 10 1.2 Multiple Sklerose 10 1.3 Klinische Verlaufsformen 12 1.4 Histopathologie 14 1.5 Immunpathogenese 15 1.6 Therapie der Multiplen Sklerose 17 1.7 Therapiemonitoring und Biomarker 18 1.7.1 Expanded Disability Status Scale 19 1.7.2 Multiple Sclerosis Functional Composite 21 1.7.3 Magnetresonanztomographie 22 1.7.4 Molekulare Biomarker 23 1.8 Glatiramerazetat 25 1.8.1 Geschichte 25 1.8.2 Therapeutische Anwendung 26 1.8.3 Wirkmechanismus 28 1.9 Ziel und Forschungsfragen 29 2 Material und Methoden 32 2.1 Patientenkollektiv 32 2.2 Klinische Kontrollen 33 2.2.1 Anamnese 33 2.2.2 Expanded Disease Disability Scale 34 2.2.3 Multiple Sclerosis Functional Composite 35 2.3 Blutentnahmen 36 2.4 Gewinnung und Verarbeitung der Plasmaproben 37 2.5 Glatiramerazetat spezifische IgM- und IgG-Messung 39 2.5.1 Enzyme-linked Immunosorbent Assay 39 2.5.2 Durchführung der GA-spezifischen IgM und IgG Messung 41 2.5.2.1 Coating und Blocken der Platten 41 2.5.2.2 Herstellung der Gebrauchslösungen 41 2.5.2.3 Inkubation mit Plasmaproben 42 2.5.2.4 Detektion und Extinktionsbestimmung 44 2.6 Neurofilament-Messung 45 2.6.1 Grundlagen der SiMoA-Technologie 46 2.6.2 Durchführung 47 2.7 Statistische Auswertung der Daten 48 3 Resultate 49 3.1 Klinische Daten 49 3.1.1 Demographische Daten 49 3.1.2 Klinisches Therapieansprechen 50 3.1.2.1 Schübe 50 3.1.2.2 Expanded Disease Disability Scale und Multiple Sclerosis Functional Composite im Therapiezeitraum 50 3.1.2.3 Klinische Response 53 3.2 Immunglobuline 57 3.2.1 Immunglobulin M 58 3.2.2 Immunglobulin G 60 3.2.3 Immunglobulin G1 62 3.2.4 Immunglobulin G2 65 3.2.5 Immunglobulin G3 66 3.2.6 Immunglobulin G4 68 3.3 Neurofilament 70 4 Diskussion 74 4.1 Klinische Parameter 75 4.2 GA-spezifische Immunglobulinproduktion 77 4.3 Auswirkungen auf den NfL-Spiegel 79 4.4 Limitationen 81 5 Fazit und Ausblick 82 Literaturverzeichnis 84 Verzeichnisse von Abbildungen und Tabellen 96 Abbildungen 96 Tabellen 98 Danksagung Eidesstattliche Versicherung Anhänge Anlagen der Medizinischen Fakultät

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